OpenRaiser/Intern-Atlas huggingface dataset:
Epi-constructivism: Decidable sets of computable numbers as foundational objects for mathematics
Venue: arXiv cs.LO 2022
Year: 2022

It is well known that the R, the set of real numbers, is an abstract set, where almost all its elements cannot be described in any finite language. We investigate possible approaches to what might be called an epi-constructionist approach to mathematics. While most constructive mathematics is concerned with constructive proofs, the agenda here is that the objects that we study, specifically the class of numbers that we study, should be an enumerable set of finite symbol strings. These might also be called decidable constructive real numbers, that is our class of numbers should be a computable sets of explicitly represented computable numbers. There have been various investigations of the computable numbers going back to Turing. Most are however not expressed constructively, rather computable is a property assigned to some of the abstract real numbers. Other definitions define constructive real numbers without reference to the abstract R, but the construction is undecidable, i.e., we cannot determine if a given construction represents a computable real number or not. For example, we may define a real as a computable convergent sequence of rationals, but cannot in general decide if a given computable sequence is convergent. This paper explores several specific classes of decidable constructive real numbers that could form foundational objects for what we might call an epi-constructionist mathematics.
2D Beam Domain Statistical CSI Estimation for Massive MIMO Uplink
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we investigate the beam domain statistical channel state information (CSI) estimation for the two dimensional (2D) beam based statistical channel model (BSCM) in massive MIMO systems.The problem is to estimate the beam domain channel power matrices (BDCPMs) based on multiple receive pilot signals. A receive model shows the relation between the statistical property of the receive pilot signals and the BDCPMs is derived from the 2D-BSCM. On the basis of the receive model,we formulate an optimization problem with the Kullback-Leibler (KL) divergence. By solving the optimization problem, a novel method to estimate the statistical CSI without involving instantaneous CSI is proposed. The proposed method has much lower complexity than the MMV focal underdetermined system solver (M-FOCUSS) algorithm. We further reduce the complexity of the proposed method by utilizing the circulant structures of particular matrices in the algorithm. We also showed the generality of the proposed method by introducing another application. Simulations results show that the proposed method works well and bring significant performance gain when used in channel estimation.
The Confluence of Blockchain and 6G Network: Scenarios Analysis and Performance Assessment
Venue: arXiv cs.DC 2022
Year: 2022

Emerging advanced applications, such as smart cities, healthcare, and virtual reality, demand more challenging requirements on sixth-generation (6G) mobile networks, including the need for improved secrecy, greater integrity, non-repudiation, authentication, and access control. While blockchain, with its intrinsic features, is generally regarded as one of the most disruptive technological enablers for 6G functional standards, there is no comprehensive study of whether, when, and how blockchain will be used in 6G scenarios. Existing research lacks performance assessment methodology for the use of blockchain in 6G scenarios. Therefore, we abstract seven fine-grained 6G possibilities from the application layer and investigate the why, what, and when issues for 6G scenarios in this work. Moreover, we provide a methodology for evaluating the performance and scalability of blockchain-based 6G scenarios. In conclusion, we undertake comprehensive experimental to assess the performance of the Quorum blockchain and 6G scenarios. The experimental results show that a consortium blockchain with the proper settings may satisfy the performance and scalability requiremen
On Computing Relevant Features for Explaining NBCs
Venue: arXiv cs.LG 2022
Year: 2022

Despite the progress observed with model-agnostic explainable AI (XAI), it is the case that model-agnostic XAI can produce incorrect explanations. One alternative are the so-called formal approaches to XAI, that include PI-explanations. Unfortunately, PI-explanations also exhibit important drawbacks, the most visible of which is arguably their size. The computation of relevant features serves to trade off probabilistic precision for the number of features in an explanation. However, even for very simple classifiers, the complexity of computing sets of relevant features is prohibitive. This paper investigates the computation of relevant sets for Naive Bayes Classifiers (NBCs), and shows that, in practice, these are easy to compute. Furthermore, the experiments confirm that succinct sets of relevant features can be obtained with NBCs.
bloomRF: On Performing Range-Queries in Bloom-Filters with Piecewise-Monotone Hash Functions and Prefix Hashing
Venue: arXiv cs.DB 2022
Year: 2022

We introduce bloomRF as a unified method for approximate membership testing that supports both point- and range-queries. As a first core idea, bloomRF introduces novel prefix hashing to efficiently encode range information in the hash-code of the key itself. As a second key concept, bloomRF proposes novel piecewise-monotone hash-functions that preserve local order and support fast range-lookups with fewer memory accesses. bloomRF has near-optimal space complexity and constant query complexity. Although, bloomRF is designed for integer domains, it supports floating-points, and can serve as a multi-attribute filter. The evaluation in RocksDB and in a standalone library shows that it is more efficient and outperforms existing point-range-filters by up to 4x across a range of settings and distributions, while keeping the false-positive rate low.
A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
Venue: arXiv cs.CV 2022
Year: 2022

Deep learning-based approaches for content-based image retrieval (CBIR) of CT liver images is an active field of research, but suffers from some critical limitations. First, they are heavily reliant on labeled data, which can be challenging and costly to acquire. Second, they lack transparency and explainability, which limits the trustworthiness of deep CBIR systems. We address these limitations by (1) proposing a self-supervised learning framework that incorporates domain-knowledge into the training procedure and (2) providing the first representation learning explainability analysis in the context of CBIR of CT liver images. Results demonstrate improved performance compared to the standard self-supervised approach across several metrics, as well as improved generalisation across datasets. Further, we conduct the first representation learning explainability analysis in the context of CBIR, which reveals new insights into the feature extraction process. Lastly, we perform a case study with cross-examination CBIR that demonstrates the usability of our proposed framework. We believe that our proposed framework could play a vital role in creating trustworthy deep CBIR systems that can successfully take advantage of unlabeled data.
On the vulnerability of fingerprint verification systems to fake fingerprint attacks
Venue: arXiv cs.CV 2022
Year: 2022

A new method to generate gummy fingers is presented. A medium-size fake fingerprint database is described and two different fingerprint verification systems are evaluated on it. Three different scenarios are considered in the experiments, namely: enrollment and test with real fingerprints, enrollment and test with fake fingerprints, and enrollment with real fingerprints and test with fake fingerprints. Results for an optical and a thermal sweeping sensors are given. Both systems are shown to be vulnerable to direct attacks.
Adaptive Behavioral Model Learning for Software Product Lines
Venue: arXiv cs.SE 2022
Year: 2022

Behavioral models enable the analysis of the functionality of software product lines (SPL), e.g., model checking and model-based testing. Model learning aims at constructing behavioral models for software systems in some form of a finite state machine. Due to the commonalities among the products of an SPL, it is possible to reuse the previously learned models during the model learning process. In this paper, an adaptive approach (the $\text{PL}^*$ method) for learning the product models of an SPL is presented based on the well-known $L^*$ algorithm. In this method, after model learning of each product, the sequences in the final observation table are stored in a repository which will be used to initialize the observation table of the remaining products to be learned. The proposed algorithm is evaluated on two open-source SPLs and the total learning cost is measured in terms of the number of rounds, the total number of resets and input symbols. The results show that for complex SPLs, the total learning cost for the $\text{PL}^*$ method is significantly lower than that of the non-adaptive learning method in terms of all three metrics. Furthermore, it is observed that the order in which the products are learned affects the efficiency of the $\text{PL}^*$ method. Based on this observation, we introduced a heuristic to determine an ordering which reduces the total cost of adaptive learning in both case studies.
An Overview on IEEE 802.11bf: WLAN Sensing
Venue: arXiv cs.NI 2022
Year: 2022

With recent advancements, the wireless local area network (WLAN) or wireless fidelity (Wi-Fi) technology has been successfully utilized to realize sensing functionalities such as detection, localization, and recognition. However, the WLANs standards are developed mainly for the purpose of communication, and thus may not be able to meet the stringent sensing requirements in emerging applications. To resolve this issue, a new Task Group (TG), namely IEEE 802.11bf, has been established by the IEEE 802.11 working group, with the objective of creating a new amendment to the WLAN standard to provide advanced sensing requirements while minimizing the effect on communications. This paper provides a comprehensive overview on the up-to-date efforts in the IEEE 802.11bf TG. First, we introduce the definition of the 802.11bf amendment and its standardization timeline. Then, we discuss the WLAN sensing procedure and framework used for measurement acquisition, by considering both conventional sensing at sub-7 GHz and directional multi-gigabit (DMG) sensing at 60 GHz, respectively. Next, we present various candidate technical features for IEEE 802.11bf, including waveform/sequence design, feedback types, quantization, as well as security and privacy. Finally, we describe the methodologies used by the IEEE 802.11bf TG to evaluate the alternative performance. It is desired that this overview paper provide useful insights on IEEE 802.11 WLAN sensing to people with great interests and promote the IEEE 802.11bf standard to be widely deployed.
Orchestrating Tool Chains for Model-based Systems Engineering with RCE
Venue: arXiv cs.SE 2022
Year: 2022

When using multiple software tools to analyze, visualize, or optimize models in MBSE, it is often tedious and error-prone to manually coordinate the execution of these tools and to retain their respective input and output data for later analysis. Since such tools often require expertise in their usage as well as diverse run-time environments, it is not straightforward to orchestrate their execution via off-the-shelf software tools. We present RCE, an application developed at the German Aerospace Center that supports engineers in developing and orchestrating the execution of complex tool chains. This application is used in numerous research and development projects in diverse domains and enables and simplifies the creation, analysis, and optimization of models.
Bayesian Optimization-based Nonlinear Adaptive PID Controller Design for Robust Mobile Manipulation
Venue: arXiv cs.RO 2022
Year: 2022

In this paper, we propose to use a nonlinear adaptive PID controller to regulate the joint variables of a mobile manipulator. The motion of the mobile base forces undue disturbances on the joint controllers of the manipulator. In designing a conventional PID controller, one should make a trade-off between the performance and agility of the closed-loop system and its stability margins. The proposed nonlinear adaptive PID controller provides a mechanism to relax the need for such a compromise by adapting the gains according to the magnitude of the error without expert tuning. Therefore, we can achieve agile performance for the system while seeing damped overshoot in the output and track the reference as close as possible, even in the presence of external disturbances and uncertainties in the modeling of the system. We have employed a Bayesian optimization approach to choose the parameters of a nonlinear adaptive PID controller to achieve the best performance in tracking the reference input and rejecting disturbances. The results demonstrate that a well-designed nonlinear adaptive PID controller can effectively regulate a mobile manipulator's joint variables while carrying an unspecified heavy load and an abrupt base movement occurs.
Hebbian Continual Representation Learning
Venue: arXiv cs.NE 2022
Year: 2022

Continual Learning aims to bring machine learning into a more realistic scenario, where tasks are learned sequentially and the i.i.d. assumption is not preserved. Although this setting is natural for biological systems, it proves very difficult for machine learning models such as artificial neural networks. To reduce this performance gap, we investigate the question whether biologically inspired Hebbian learning is useful for tackling continual challenges. In particular, we highlight a realistic and often overlooked unsupervised setting, where the learner has to build representations without any supervision. By combining sparse neural networks with Hebbian learning principle, we build a simple yet effective alternative (HebbCL) to typical neural network models trained via the gradient descent. Due to Hebbian learning, the network have easily interpretable weights, which might be essential in critical application such as security or healthcare. We demonstrate the efficacy of HebbCL in an unsupervised learning setting applied to MNIST and Omniglot datasets. We also adapt the algorithm to the supervised scenario and obtain promising results in the class-incremental learning.
SDFEst: Categorical Pose and Shape Estimation of Objects from RGB-D using Signed Distance Fields
Venue: arXiv cs.CV 2022
Year: 2022

Rich geometric understanding of the world is an important component of many robotic applications such as planning and manipulation. In this paper, we present a modular pipeline for pose and shape estimation of objects from RGB-D images given their category. The core of our method is a generative shape model, which we integrate with a novel initialization network and a differentiable renderer to enable 6D pose and shape estimation from a single or multiple views. We investigate the use of discretized signed distance fields as an efficient shape representation for fast analysis-by-synthesis optimization. Our modular framework enables multi-view optimization and extensibility. We demonstrate the benefits of our approach over state-of-the-art methods in several experiments on both synthetic and real data. We open-source our approach at https://github.com/roym899/sdfest.
Proposal and Verification of Novel Machine Learning on Classification Problems
Venue: arXiv cs.NE 2022
Year: 2022

This paper aims at proposing a new machine learning for classification problems. The classification problem has a wide range of applications, and there are many approaches such as decision trees, neural networks, and Bayesian nets. In this paper, we focus on the action of neurons in the brain, especially the EPSP/IPSP cancellation between excitatory and inhibitory synapses, and propose a Machine Learning that does not belong to any conventional method. The feature is to consider one neuron and give it a multivariable Xj (j = 1, 2,.) and its function value F(Xj) as data to the input layer. The multivariable input layer and processing neuron are linked by two lines to each variable node. One line is called an EPSP edge, and the other is called an IPSP edge, and a parameter Δj common to each edge is introduced. The processing neuron is divided back and forth into two parts, and at the front side, a pulse having a width 2Δj and a height 1 is defined around an input X . The latter half of the processing neuron defines a pulse having a width 2Δj centered on the input Xj and a height F(Xj) based on a value obtained from the input layer of F(Xj). This information is defined as belonging to group i. In other words, the group i has a width of 2Δj centered on the input Xj, is defined in a region of height F(Xj), and all outputs of xi within the variable range are F(Xi). This group is learned and stored by a few minutes of the Teaching signals, and the output of the TEST signals is predicted by which group the TEST signals belongs to. The parameter Δj is optimized so that the accuracy of the prediction is maximized. The proposed method was applied to the flower species classification problem of Iris, the rank classification problem of used cars, and the ring classification problem of abalone, and the calculation was compared with the neural networks.
Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utility
Venue: arXiv cs.LG 2022
Year: 2022

Bottleneck problems are an important class of optimization problems that have recently gained increasing attention in the domain of machine learning and information theory. They are widely used in generative models, fair machine learning algorithms, design of privacy-assuring mechanisms, and appear as information-theoretic performance bounds in various multi-user communication problems. In this work, we propose a general family of optimization problems, termed as complexity-leakage-utility bottleneck (CLUB) model, which (i) provides a unified theoretical framework that generalizes most of the state-of-the-art literature for the information-theoretic privacy models, (ii) establishes a new interpretation of the popular generative and discriminative models, (iii) constructs new insights to the generative compression models, and (iv) can be used in the fair generative models. We first formulate the CLUB model as a complexity-constrained privacy-utility optimization problem. We then connect it with the closely related bottleneck problems, namely information bottleneck (IB), privacy funnel (PF), deterministic IB (DIB), conditional entropy bottleneck (CEB), and conditional PF (CPF). We show that the CLUB model generalizes all these problems as well as most other information-theoretic privacy models. Then, we construct the deep variational CLUB (DVCLUB) models by employing neural networks to parameterize variational approximations of the associated information quantities. Building upon these information quantities, we present unified objectives of the supervised and unsupervised DVCLUB models. Leveraging the DVCLUB model in an unsupervised setup, we then connect it with state-of-the-art generative models, such as variational auto-encoders (VAEs), generative adversarial networks (GANs), as well as the Wasserstein GAN (WGAN), Wasserstein auto-encoder (WAE), and adversarial auto-encoder (AAE) models through the optimal transport (OT) problem. We then show that the DVCLUB model can also be used in fair representation learning problems, where the goal is to mitigate the undesired bias during the training phase of a machine learning model. We conduct extensive quantitative experiments on colored-MNIST and CelebA datasets, with a public implementation available, to evaluate and analyze the CLUB model.
Simple Dynamic Spanners with Near-optimal Recourse against an Adaptive Adversary
Venue: arXiv cs.DS 2022
Year: 2022

Designing dynamic algorithms against an adaptive adversary whose performance match the ones assuming an oblivious adversary is a major research program in the field of dynamic graph algorithms. One of the prominent examples whose oblivious-vs-adaptive gap remains maximally large is the \emph{fully dynamic spanner} problem; there exist algorithms assuming an oblivious adversary with near-optimal size-stretch trade-off using only $\operatorname{polylog}(n)$ update time [Baswana, Khurana, and Sarkar TALG'12; Forster and Goranci STOC'19; Bernstein, Forster, and Henzinger SODA'20], while against an adaptive adversary, even when we allow infinite time and only count recourse (i.e. the number of edge changes per update in the maintained spanner), all previous algorithms with stretch at most $\log^{5}(n)$ require at least $Ω(n)$ amortized recourse [Ausiello, Franciosa, and Italiano ESA'05]. In this paper, we completely close this gap with respect to recourse by showing algorithms against an adaptive adversary with near-optimal size-stretch trade-off and recourse. More precisely, for any $k\ge1$, our algorithm maintains a $(2k-1)$-spanner of size $O(n^{1+1/k}\log n)$ with $O(\log n)$ amortized recourse, which is optimal in all parameters up to a $O(\log n)$ factor. As a step toward algorithms with small update time (not just recourse), we show another algorithm that maintains a $3$-spanner of size $\tilde O(n^{1.5})$ with $\operatorname{polylog}(n)$ amortized recourse \emph{and} simultaneously $\tilde O(\sqrt{n})$ worst-case update time.
Fair Throughput Optimization with a Dynamic Network of Drone Relays
Venue: arXiv cs.NI 2022
Year: 2022

Aiding the ground cellular network with aerial base stations carried by drones has experienced an intensive raise of interest in the past years. Reconfigurable air-to-ground channels enable aerial stations to enhance users access links by means of seeking good line-of-sight connectivity while hovering in the air. In this paper, we propose an analytical framework for the 3D placement of a fleet of coordinated drone relays. This framework optimizes network performance in terms of user throughput fairness, expressed through the α-fairness metric. The optimization problem is formulated as a mixed-integer non-convex program, which is intractable. Hence, we propose an extremal-optimization-based algorithm, Parallelized Alpha-fair Drone Deployment, that solves the problem online, in low-degree polynomial time. We evaluate our proposal by means of numerical simulations over the real topology of a dense city. We discuss the advantages of integrating drone relay stations in current networks and test several resource scheduling approaches in both static and dynamic scenarios, including with progressively larger and denser crowds.
Differentiable Physics Simulations with Contacts: Do They Have Correct Gradients w.r.t. Position, Velocity and Control?
Venue: arXiv cs.LG 2022
Year: 2022

In recent years, an increasing amount of work has focused on differentiable physics simulation and has produced a set of open source projects such as Tiny Differentiable Simulator, Nimble Physics, diffTaichi, Brax, Warp, Dojo and DiffCoSim. By making physics simulations end-to-end differentiable, we can perform gradient-based optimization and learning tasks. A majority of differentiable simulators consider collisions and contacts between objects, but they use different contact models for differentiability. In this paper, we overview four kinds of differentiable contact formulations - linear complementarity problems (LCP), convex optimization models, compliant models and position-based dynamics (PBD). We analyze and compare the gradients calculated by these models and show that the gradients are not always correct. We also demonstrate their ability to learn an optimal control strategy by comparing the learned strategies with the optimal strategy in an analytical form. The codebase to reproduce the experiment results is available at https://github.com/DesmondZhong/diff_sim_grads.